<html>
   <head>
      <meta http-equiv="Content-Type" content="text/html; charset=utf-8">
   
      <link rel="stylesheet" href="./../helpwin.css">
      <title>MATLAB File Help: prtScoreRoc</title>
   </head>
   <body>
      <!--Single-page help-->
      <table border="0" cellspacing="0" width="100%">
         <tr class="subheader">
            <td class="headertitle">MATLAB File Help: prtScoreRoc</td>
            
            
         </tr>
      </table>
      <div class="title">prtScoreRoc</div>
      <div class="helptext"><pre><!--helptext -->  <span class="helptopic">prtScoreRoc</span>   Generate a reciever operator characteristic curve
 
     <span class="helptopic">prtScoreRoc</span>(DECSTATS,LABELS) plots the receiver operator
     characteristic curve for the decision statistics DECSTATS and the
     corresponding labels LABELS. DECSTATS must be a Nx1 vector of decision
     statistics. LABELS must be a Nx1 vector of binary class labels.
 
     [PF, PD, THRESHOLDS, AUC] = <span class="helptopic">prtScoreRoc</span>(DECSTATS,LABELS) outputs the
     probability of false alarm PF, the probability of detection PD, the
     THREHSOLDS required to achieved each PF and PD, and the area under the
     ROC curve AUC.
 
     [PF, PD, THRESHOLDS, AUC] = <span class="helptopic">prtScoreRoc</span>(DECSTATSMAT,LABELS) outputs
     the roc outputs for each column of DECSTATSMAT using. The output
     values are now cell arrays containing the outputs as if <span class="helptopic">prtScoreRoc</span>
     were called on each column of DECSTATSMAT individually.
 
     [...] = <span class="helptopic">prtScoreRoc</span>(PRTDATASETCLASS) use a prtDataSetClass as input
     DECSTATS is PRTDATASETCLASS.getObservations() and LABELS is
     PRTDATASETCLASS.getTargets(). If PRTDATASETCLASS.nFeatures &gt; 1 cell
     arrays are provided as outputs.
 
 
     Example:
     TestDataSet = prtDataGenSpiral;       % Create some test and
     TrainingDataSet = prtDataGenSpiral;   % training data
     classifier = prtClassSvm;             % Create a classifier
     classifier = classifier.train(TrainingDataSet);    % Train
     classified = run(classifier, TestDataSet);
     %  Plot the ROC
     <span class="helptopic">prtScoreRoc</span>(classified.getX, TestDataSet.getY);</pre></div><!--after help --><!--seeAlso--><div class="footerlinktitle">See also</div><div class="footerlink"> <a href="./prtScoreConfusionMatrix.html">prtScoreConfusionMatrix</a>, <a href="./prtScoreRmse.html">prtScoreRmse</a>, <a href="./prtScoreRocNfa.html">prtScoreRocNfa</a>,
             <a href="./prtScorePercentCorrect.html">prtScorePercentCorrect</a>, <a href="./prtScoreAuc.html">prtScoreAuc</a>
</div>
   </body>
</html>